University of Essex School of Computer Science and Electronic Engineering CE802 Machine Learning and Data Mining Assignment: Design and Application of a Machine Learning System for a Practical Problem Set by: Dr Luca Citi ([email protected]) Distributed to students: week 10 Submission deadline: week 16 (see FASer for exact official date and time) Submission mode: electronic only via FASer Assignment objectives This document specifies the coursework assignment to be submitted by students taking CE802. Aims of this assignment are: a) to learn to identify machine learning techniques appropriate for a particular practical problem; and b) to undertake a comparative evaluation of several machine learning procedures when applied to the specific problem. Assignment description 1. Pilot-Study Proposal Imagine that you work as Machine Learning independent consultant, providing scientific advisory and consulting services to companies seeking to apply data analytics to their business activities. A travel insurance company would like to offer a discounted premium (for the same cover re- quested) to customers that are less likely to make a future claim. The manager contacts you to investigate the feasibility of using machine learning to predict whether a customer will file a claim on their travel. The manager has access to historical data of past policies and she offers to provide you with information about the insured, the purpose and destination of the travel, and whether a claim was filed. In the first part of your assignment, you are asked to write a detailed proposal for a pilot study to investigate whether machine learning procedures could be used to successfully solve this problem. Your report should discuss several aspects of the problem, including the following main points: the type of predictive task that must be performed (e.g., classification, regression, clustering, rules mining, …); examples of possibly informative features that you would like to be provided with (what type of information that the company could obtain from/about the customer is likely to be a good predictor?); the learning procedure or procedures (e.g., DTs, k-NN, k-means, linear regression, Apriori, SVMs, …) you would choose and the reason for your choice; how you would evaluate the performance of your system before deploying it. 1 You can assume that the manager has some knowledge of machine learning and you do not need to explain how the recommended learning method works. Simply discuss your recommendation and back it with sound arguments and/or references. This document should consist of approximately 500–750 words of narrative (i.e. excluding references, pictures, and diagrams). Please report your word count on the title page. The document must be submitted in PDF format with file name CE802_Pilot.pdf. 2. Comparative Study Thanks to the convincing arguments in your pilot-study proposal, the company decides to collect the data that you suggested and to hire you to perform the proposed study. They provide you with a training set of historical data containing features of each customer and a label representing whether the insured filed a claim. These data are available in the CE802_P2_Data.zip archive available from the CE802 moodle page. In this part of the assignment, you are asked to perform the following two tasks. a) Investigate the performance of a number of machine learning procedures on this dataset. Using the data in the file CE802_P2_Data.csv contained in the CE802_P2_Data.zip archive, you are required to perform a comparative study of the following machine learning proce- dures: a Decision Tree classifier; at least two more ML technique to predict if the insured will file a claim. You will notice that one of the features, is missing for some of the instances. You are therefore required to deal with the problem of missing features before you can proceed with the prediction step. As a baseline approach you may try to discard the feature altogether and train on the remaining features. You are then encouraged to experiment with different inputation methods. The company uses Python internally and therefore Python with scikit-learn is the required language and machine learning library for the problem. For this task, you are expected to submit a Jupyter Notebook called CE802_P2_Notebook.ipynb containing the Python code used to perform the comparative analysis and produce the results, as well as the code used to perform the predictions described in task “b” below. b) Prediction on a hold-out test set. An additional dataset, CE802_P2_Test.csv, is provided inside the CE802_P2_Data.zip archive. Binary outcomes are withheld for this test set (i.e. the “Class” column is empty). In this second task you are required to produce class predictions of the records in the test set using one approach of your choice among those tested in task “a” (for example the one achieving the best performance). These data must not be used other than to test the algorithm trained on the training data. As part of your submission you should submit a new version of the file CE802_P2_Test.csv in CSV format with the missing class replaced with the output predictions obtained using the approach chosen. This second task will be marked based on the prediction accuracy on the test set. 3. Additional Comparative Study Thanks to the good results obtained in the comparative study, the company has deployed your system and is obtaining good profit. Now a competitor would like to hire you to design a similar system for them but, unlike the first system, they would like you to predict not only if the insured files a claim but also the value of the claim. They provide you with a training set of historical data containing features of each customer and a numerical value representing the value of the claim (which may be zero). These data are available in the CE802_P3_Data.zip archive available from the CE802 moodle page. In this part of the assignment, you are asked to perform the following two tasks. 2 a) Investigate the performance of a number of machine learning procedures on this dataset. Using the data in the file CE802_P3_Data.csv contained in the CE802_P3_Data.zip archive, you are required to perform a comparative study of the following machine learning proce- dures: Linear Regression; at least two more ML technique to predict the value of the claim. This company too uses Python internally and therefore Python with scikit-learn is the required language and machine learning library for the problem. For this task, you are expected to submit a Jupyter Notebook called CE802_P3_Notebook.ipynb containing the Python code used to perform the comparative analysis and produce the results as well as the code used to perform the predictions described in task “b” below. b) Prediction on a hold-out test set. An additional dataset, CE802_P3_Test.csv, is provided inside the CE802_P3_Data.zip archive. Target values are withheld for this test set (i.e. the “Value” column is empty). In this second task you are required to produce predictions of the records in the test set using one approach of your choice among those tested in task “a” (for example the one achieving the best performance). These data must not be used other than to test the algorithm trained on the training data. As part of your submission you should submit a new version of the file CE802_P3_Test.csv in CSV format with the missing “Value” column replaced with the output predictions obtained using the approach chosen. This second task will be marked based on the mean squared error on the test set. 4. Report on the Investigation After conducting the studies in parts 2 and 3, you are asked to write a report containing an account of your investigation. There should be a brief summary of the experiments performed followed by one or more tables and/or graphs summarizing the performance of the different solutions. Any numerical data that you include should be in a suitable graphical or tabular form. The rest of the report should concentrate on your interpretation of the results and what they tell you abo
ut the relative strengths and weaknesses of the alternative methods when applied to the given data. This document should consist of approximately 750–1500 words of narrative (i.e. excluding references, pictures, and diagrams). Please report your word count on the title page. The document must be submitted in PDF format with file name CE802_Report.pdf. Suggested material Scikit-learn online documentation and tutorials: http://scikit-learn.org. Lecture notes on machine learning and Lab notes on scikit-learn, pandas (to read/write CSV files): see CE802 moodle page. Submission Your work must be submitted to the university’s online coursework submission system at the address http://faser.essex.ac.uk/ by the deadline given on the system. No other mode of submission is acceptable. You are strongly advised to submit a draft submission well before the deadline, and then update it up to the deadline. You are required to submit the following files on FASER: 1. The file CE802_Pilot.pdf with the pilot study document exported to PDF format (no doc or docx, etc.); 3 欢迎咨询51作业君
